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Deep High-Resolution Representation Learning for Human Pose Estimation

Introduction

This repo is TensorFlow implementation of Deep High-Resolution Representation Learning for Human Pose Estimation (CVPR 2019) of MSRA for 2D multi-person pose estimation from a single RGB image.

Dependencies

Python 3.7.1 version with Anaconda 3 is used for development.

Directory

Root

The ${POSE_ROOT} is described as below.

${POSE_ROOT}
|-- core
|-- data
|-- hrnet
|-- nms
|-- tfflat
|-- tools
  • core contains codes for train engine, test engine, basic dataloader and the pose model.
  • data contains the codes for data processing on COCO, MPII and PoseTrack.
  • hrnet contains codes for building HRNets.
  • nms contains codes for NMS.
  • tfflat contains useful codes for data parallel, multi-gpu training, model saving and loading.
  • tools contains codes that convert MPII and PoseTrack data into COCO format.

Dataset

You need prepare the data in the dataset directory as below.

${POSE_ROOT}
|-- dataset
    |-- MPII
    |-- |-- dets
    |   |   |-- human_detection.json
    |   |-- annotations
    |   |   |-- train.json
    |   |   `-- test.json
    |   |-- images
    |   |   |-- 000001163.jpg
    |   `-- mpii2coco.py
    |
    |-- PoseTrack
    |-- |-- dets
    |   |   |-- human_detection.json
    |   |-- annotations
    |   |   |-- train2018.json
    |   |   |-- val2018.json
    |   |   `-- test2018.json
    |   |-- original_annotations
    |   |   |-- train/
    |   |   |-- val/
    |   |   `-- test/
    |   |-- images
    |   |   |-- train/
    |   |   |-- val/
    |   |   `-- test/
    |   `-- posetrack2coco.py
    |
    |-- COCO
    |-- |-- dets
    |   |   |-- human_detection.json
    |   |-- annotations
    |   |   |-- person_keypoints_train2017.json
    |   |   |-- person_keypoints_val2017.json
    |   |   `-- image_info_test-dev2017.json
    |   `-- images
    |       |-- train2017/
    |       |-- val2017/
    |       `-- test2017/
    |
    `-- imagenet_weights
        |-- hrnet_w30.ckpt
        |-- hrnet_w32.ckpt
        |-- hrnet_w48.ckpt
  • Creating dataset folder (or its sub-folders) as soft link(s) form is recommended instead of folder form because it would take large storage capacity.
  • Use the python script in ${POSE_ROOT}/tools/ to convert MPII and PoseTrack annotation files to MS COCO format.
  • If you want to add your own dataset, you have to convert it to MS COCO format.
  • Except for annotations of the MPII and PoseTrack, all other directories are original version of downloaded ones.
  • In the training stage, GT human bbox is used, and human_detection.json is used in testing stage which should be prepared before testing and follow MS COCO format.
  • Download imagenet pre-trained hrnet models from here and place them in the data/imagenet_weights.

Output

You need to follow the directory structure of the output folder as below.

${POSE_ROOT}
|-- output
    |-- log
    |-- model_dump
    `-- result
  • Creating output folder as soft link form is recommended instead of folder form because it would take large storage capacity.
  • log folder contains training log file.
  • model_dump folder contains saved checkpoints for each epoch.
  • result folder contains final estimation files generated in the testing stage.
  • You can change default directory structure of output by modifying main/config.py.

Run

Start

  • Install the dependencies.
  • Run pip install -r requirements.txt to install other required modules.
  • Run python setup.py build_ext --inplace; rm -rf build in ${POSE_ROOT}/nms to build NMS modules.
  • In the ${POSE_ROOT}/config.py, you can change settings of the model including dataset to use, network backbone, input size and so on.

Train

In ${POSE_ROOT}, run

python train.py --gpu 0,1,2,3

to train the network on the GPU 0,1,2,3.

If you want to continue experiment, run

python train.py --gpu 0,1,2,3 --continue

and the training will restart from the latest checkpoint.

Test

Place trained model at the cfg.model_dump_dir and human detection result (human_detection.json) at dataset/$DATASET/dets/.

In ${POSE_ROOT}, run

python test.py --gpu 0,1,2,3 --test_epoch 210

to test the network on the GPU 0,1,2,3 with 210th epoch trained model.